






'''simple docstring'''




import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler

class   _UpperCAmelCase     (	snake_case_		):




   """simple docstring"""

   def __init__(						self					:      Optional[Any] , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      int , __UpperCAmelCase					:      List[str] , ):





         '''simple docstring'''





         super().__init__()
         _A     	=      value_function
         _A     	=      unet
         _A     	=      scheduler
         _A     	=      env
         _A     	=      env.get_dataset()
         _A     	=      {}
         for key in self.data.keys():
               try:
                     _A     	=      self.data[key].mean()
               except:  # noqa: E722
                     pass
         _A     	=      {}
         for key in self.data.keys():
               try:
                     _A     	=      self.data[key].std()
               except:  # noqa: E722
                     pass
         _A     	=      env.observation_space.shape[0]
         _A     	=      env.action_space.shape[0]

   def 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      Tuple						):





         '''simple docstring'''





         return (x_in - self.means[key]) / self.stds[key]

   def 		lowerCAmelCase					(						self					:      int , __UpperCAmelCase					:      Union[str, Any] , __UpperCAmelCase					:      Optional[Any]						):





         '''simple docstring'''





         return x_in * self.stds[key] + self.means[key]

   def 		lowerCAmelCase					(						self					:      Union[str, Any] , __UpperCAmelCase					:      List[Any]						):





         '''simple docstring'''





         if type(__UpperCAmelCase						) is dict:
               return {k: self.to_torch(__UpperCAmelCase						) for k, v in x_in.items()}
         elif torch.is_tensor(__UpperCAmelCase						):
               return x_in.to(self.unet.device						)
         return torch.tensor(__UpperCAmelCase , device=self.unet.device						)

   def 		lowerCAmelCase					(						self					:      Dict , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      Any , __UpperCAmelCase					:      Dict						):





         '''simple docstring'''





         for key, val in cond.items():
               _A     	=      val.clone()
         return x_in

   def 		lowerCAmelCase					(						self					:      Tuple , __UpperCAmelCase					:      Union[str, Any] , __UpperCAmelCase					:      Optional[int] , __UpperCAmelCase					:      Optional[int] , __UpperCAmelCase					:      Optional[Any]						):





         '''simple docstring'''





         _A     	=      x.shape[0]
         _A     	=      None
         for i in tqdm.tqdm(self.scheduler.timesteps						):
               # create batch of timesteps to pass into model
               _A     	=      torch.full((batch_size,) , __UpperCAmelCase , device=self.unet.device , dtype=torch.long						)
               for _ in range(__UpperCAmelCase						):
                     with torch.enable_grad():
                           x.requires_grad_()

                           # permute to match dimension for pre-trained models
                           _A     	=      self.value_function(x.permute(0 , 2 , 1						) , __UpperCAmelCase						).sample
                           _A     	=      torch.autograd.grad([y.sum()] , [x]						)[0]

                           _A     	=      self.scheduler._get_variance(__UpperCAmelCase						)
                           _A     	=      torch.exp(0.5 * posterior_variance						)
                           _A     	=      model_std * grad

                     _A     	=      0
                     _A     	=      x.detach()
                     _A     	=      x + scale * grad
                     _A     	=      self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim						)

               _A     	=      self.unet(x.permute(0 , 2 , 1						) , __UpperCAmelCase						).sample.permute(0 , 2 , 1						)

               # TODO: verify deprecation of this kwarg
               _A     	=      self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , predict_epsilon=__UpperCAmelCase						)["prev_sample"]

               # apply conditions to the trajectory (set the initial state)
               _A     	=      self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim						)
               _A     	=      self.to_torch(__UpperCAmelCase						)
         return x, y

   def __call__(						self					:      Tuple , __UpperCAmelCase					:      str , __UpperCAmelCase					:      Any=64 , __UpperCAmelCase					:      Optional[int]=32 , __UpperCAmelCase					:      Any=2 , __UpperCAmelCase					:      Optional[Any]=0.1						):





         '''simple docstring'''





         _A     	=      self.normalize(__UpperCAmelCase , "observations"						)
         _A     	=      obs[None].repeat(__UpperCAmelCase , axis=0						)

         _A     	=      {0: self.to_torch(__UpperCAmelCase						)}
         _A     	=      (batch_size, planning_horizon, self.state_dim + self.action_dim)

         # generate initial noise and apply our conditions (to make the trajectories start at current state)
         _A     	=      randn_tensor(__UpperCAmelCase , device=self.unet.device						)
         _A     	=      self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim						)
         _A     	=      self.to_torch(__UpperCAmelCase						)

         # run the diffusion process
         _A	,      _A     	=      self.run_diffusion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase						)

         # sort output trajectories by value
         _A     	=      y.argsort(0 , descending=__UpperCAmelCase						).squeeze()
         _A     	=      x[sorted_idx]
         _A     	=      sorted_values[:, :, : self.action_dim]
         _A     	=      actions.detach().cpu().numpy()
         _A     	=      self.de_normalize(__UpperCAmelCase , key="actions"						)

         # select the action with the highest value
         if y is not None:
               _A     	=      0
         else:
               # if we didn't run value guiding, select a random action
               _A     	=      np.random.randint(0 , __UpperCAmelCase						)

         _A     	=      denorm_actions[selected_index, 0]
         return denorm_actions






"""simple docstring"""



from typing import Union

import fire
import torch
from tqdm import tqdm



def 	_lowerCamelCase(	a   ,    a = "cpu"   ,    a = None				):
       __a       =    torch.load(a   ,    map_location=a				)
       for k, v in tqdm(state_dict.items()				):
              if not isinstance(a   ,    torch.Tensor				):
                     raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"				)
              __a       =    v.half()
       if save_path is None:  # overwrite src_path
              __a       =    src_path
       torch.save(a   ,    a				)


if __name__ == "__main__":
   fire.Fire(convert)
